SPP1 and the risk score model to improve the survival prediction of patients with hepatocellular carcinoma based on multiple algorithms and back propagation neural networks

Hepatocellular carcinoma(HCC)is associated with poor prognosis and fluctuations in immune status.Although studies have found that secreted phosphoprotein 1(SPP1)is involved in HCC progression,its independent prognostic value and immune-mediated role remain unclear.Using The Cancer Genome Atlas and Gene Expression Omnibus data,we found that low expression of SPP1 is significantly associated with improved survival of HCC patients and that SPP1 expression is correlated with clinical characteristics.Univariate and multivariate Cox regression confirmed that SPP1 is an independent prognostic factor of HCC.Subsequently,we found that T cell CD4 memory-activated monocytes,M0 macrophages,and resting mast cells showed significant differences in penetration in the high and low SPP1 expression groups.Next,we used the Weighted Gene Co-Expression Network and Least Absolute Shrinkage Sum Selection Operator algorithms to construct a risk score for the 9-immune-related genes signature.The risk score showed a good ability to identify high and low-risk patients and improved survival prediction.We also used multivariate Cox regression to validate that risk score was significantly correlated with SPP1 and overall survival.Lastly,the Back-Propagation Neural Network confirmed the reliability of the results of multiple algorithms.In conclusion,the findings suggest that SPP1 is an independent marker of HCC survival and immunotherapy.

WGCNA and LASSO algorithm constructed an immune infiltration-related 5-gene signature and nomogram to improve prognosis prediction of hepatocellular carcinoma

Hepatocellular carcinoma(HCC)is a common immunogenic malignant tumor.Although the new strategies of immunotherapy and targeted therapy have made considerable progress in the treatment of HCC,the 5-year survival rate of patients is still very low.The identification of new prognostic signatures and the exploration of the immune microenvironment are crucial to the optimization and improvement of molecular therapy strategies.We studied the potential clinical benefits of the inflammation regulator miR-93-3p and mined its target genes.Weighted gene coexpression network analysis(WGCNA),univariate and multivariate COX regression and the LASSO COX algorithm are employed to identify prognostic-related genes and construct multi-gene signature-based risk model and nomogram for survival prediction.Support vector machine(SVM)based Cibersort’s deconvolution algorithm and gene set enrichment analysis(GSEA)is used to evaluate the changes in tumor immune microenvironment and pathway differences.The study found the favorable prognostic performance of miR-93-3p and identified 389 prognostic-related target genes.The risk model based on a novel 5-gene signature(cct5,cdk4,cenpa,dtnbp1 and flvcr1)was developed and has prominent prognostic significance in the training cohort(P<0.0001)and validation cohort(P=0.0016).The nomogram constructed by combining the gene signature and the AJCC stage further improves the survival prediction ability of the gene signature.The infiltration level of multiple immune cells(especially T cells,B cells and macrophages)were positively correlated with the expression of prognostic signature.In addition,we found that gene markers of T cells and B cells is monitored and regulated by prognostic signature.Meanwhile,several GSEA pathways related to the immune system are enriched in the high-risk group.In general,we integrated the WGCNA,LASSO COX and SVM algorithms to develop and verify 5-gene signatures and nomograms related to immune infiltration to improve the survival prediction of patients.

An integrated bioinformatics analysis and experimental study identified key biomarkers CD300A or CXCL1,pathways and immune infiltration in diabetic nephropathy mice

Diabetic nephropathy(DN)is a common microvascular complication that easily leads to end-stage renal disease.It is important to explore the key biomarkers andmolecular mechanisms relevant to diabetic nephropathy(DN).We used highthroughput RNA sequencing to obtain the genes related to DN glomerular tissues and healthy glomerular tissues of mice.Then we used LIMMA to analyze differentially expressed genes(DEGs)between DN and non-diabetic glomerular samples.And we performed KEGG,gene ontology functional(GO)enrichment,and gene set enrichment analysis to reveal the signaling pathway of the disease.The CIBERSORT algorithm based on support vector machine was used to determine the immune infiltration score.Random forest algorithm and Cytoscape obtained hub genes.Finally,we applied co-staining,immunohistochemical staining,RT-qPCR and western blotting to validate the protein and mRNA expression of both hub genes.We obtained 913 DEGs mainly related to inflammatory factors and immunity.GSEA results showed that differential genes were mainly enriched in IL-17 signaling pathway,lipid and atherosclerosis,rheumatoid arthritis,TNF signaling pathway,neutrophil extracellular trap formation,Staphylococcus aureus infection and other pathways.The intersection of the random forest algorithm and Cytoscape revealed both hub genes of CD300A and CXCL1.Experiments have shown that the both key genes of CD300A and CXCL1 shown increased expression in glomerular podocytes,and are related to the inflammation of diabetic nephropathy.And immunohistochemical staining and RT-qPCR further confirmed that the protein and mRNA expression level of CD300A or CXCL1 in glomeruli tissue in DN mice were increased.The expression levels of CD300A and CXCL1 increased significantly under HG(high glucose)stimulation,further confirming that diabetes can lead to increased levels of CD300A and CXCL1 at the cellular level.Through bioinformatics analysis,machine learning algorithms,and experimental research,CD300A and CXCL1 are confirmed as both potential biomarkers in diabetic nephropathy.And we further revealed the main pathways of differential genes and the differentially distributed immune infiltrating cells in diabetic nephropathy.

Molecularmechanisms of Tanshinone IIA in Hepatocellular carcinoma therapy via WGCNA-based network pharmacology analysis

Hepatocellular carcinoma(HCC)is a worldwide malignant tumor that caused irreversible consequences.Tanshinone IIA has been shown to play a notable role in HCC treatment.However,the potential targets and associating mechanism of Tanshinone IIA against HCC remain unknown.We first screened out 105 overlapping genes by integrating the predicted targets of Tanshinone IIA from multiple databases and the differentially expressed genes of HCC from the Cancer Genome Atlas(TCGA)database.Then,we performed weighted gene co-expression network analysis(WGCNA)using the RNA-seq profiles of overlapping genes and HCC-related clinical information.23 genes related to clinical tumor grade in the important module were imported for Gene Ontology(GO)enrichment,Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis and protein-protein interaction(PPI)analysis.Comparing the key genes in the important module from WGCNA with the high connectivity nodes from the PPI network,we identified three hub genes,AURKB,KIF11,and PLK1.For further verification,we tested the binding of Tanshinone IIA to three hub genes.The survival curve,receiver operating characteristic(ROC)curve,mRNA expression,and protein expression were also used to validate the hub genes.In the study,WGCNA revealed gradespecific gene modules,and the following KEGG pathway analysis indicated that Tanshinone IIA probably plays therapeutical effect in the development of HCC,especially in the cell cycle.Our result partially explained the pharmacological mechanism of Tanshinone IIA against HCC.

Immune-related DNA methylation signature associated with APLN expression predicts prognostic of hepatocellular carcinoma

TIME,Immunity,Prognosis,BioinformaticsThis study used transcriptome and epigenetic data to predict the prognosis of immune-related genes(IRGs)Apelin(APLN)in patients with hepatocellular carcinoma(HCC).The TCGA database has gene expression and clinical data for HCC.And DNA methylation 450 k data for HCC was download from the University of California Santa Cruz(UCSC)Xena browser.Performing clinical and prognostic analysis of APLN expression,results show that APLN is highly expressed in tumor samples.And it has an increasing trend with the development of clinical stage and T stage.To explore the prognostic role of APLN,the Immune-related DNA methylation(DNAm)sites associated with APLN analyzed by bioinformatics.Univariate COX screened the methylation sites that are related to both APLN and survival.The risk score related to methylation site signature was determined according to their least absolute shrinkage and selection operator(LASSO)coefficients.Then the patients were divided into high-risk groups and low-risk groups.Significant differences in overall survival(OS)were found in the training cohort.Nomogram shows that APLN or methylation signature can effectively predict the prognosis of HCC patients.In summary,APLN may be a diagnostic and prognostic marker for HCC.

A novel prognostic target-gene signature and nomogram based on an integrated bioinformatics analysis in hepatocellular carcinoma

There is currently no effective solution to the problem of poor prognosis and recurrence of HCC.The technology of immunotherapy and prognosis of genetic material has made continuous progress in recent years.In the study,a 5-gene signature was established for the prognosis of HCC through biological information,and the immune infiltration of HCC patients was studied.After studied HCC patients’immune infiltration,the paper screened the differential target genes of miR-126-3p in HCC downloaded from TCGA database,and uses WGCNA method to select the modular genes highly relevant to M2 macrophage.Then we use LASSO and COX regression analysis technology to establish the 5-gene signature.The nomogram is established by combining the prognostic score and clinical phenotype.Cibersort was empolyed to observe the immune infiltration in HCC patients.We revealed the biological pathways of HCC-related genes through GSEA and Metascape.The bioinformatics analysis of 2495 differential target genes finally constructed a 5-gene signature with a reliable prognostic ability(CDCA8,SLC41A3,PPM1G,TCOF1,GRPEL2).The combination of prognostic score and AJCC_Stage resulted in a more reliable prognosis ability.At the same time,10 immune cells that are differentially expressed in HCC patients were also found.8 GSEA pathways related to the prognosis were found.In the study,a reliable 5-gene signature was established based on the differential target gene of miR-126-3p to study the immune infiltration in HCC patients.It provides help for HCC-related prognosis research and immunotherapy.